System and method of automated routing and guidance based on continuous customer and customer service representative feedback

ABSTRACT

The present invention is a system and method of routing incoming communications to a CSR and providing guidance to the CSR based on the incoming communication using feedback information such as sentiment feedback, survey feedback, and feedback from actions taken by CSRs based on skill level. A CEC system receives an incoming communication, analyzes the communication and creates metadata based off of the analysis. The metadata is used by the RAE routing module to route the communication to an appropriate party. The metadata is also used by the GAE guidance module to determine the guidance to provide to the CSR. The CSR then performs an action based on the guidance. The CEC system continues to monitor the interaction until the interaction is completed. The communication metadata, the communication, the guidance, and the CSRs action are all provided to a RAS rules analysis module wherein the RAS analyzes the data and automatedly updates the rules (RAR and GAR) according to the analysis.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.17/341,968, filed Jun. 8, 2021, which application is a continuation ofU.S. application Ser. No. 17/021,603, filed Sep. 15, 2020, whichapplication is a continuation of U.S. application Ser. No. 16/837,685,filed Apr. 1, 2020, which application is a continuation of U.S.application Ser. No. 16/225,828, filed Dec. 19, 2018, the contents ofwhich are incorporated herein by reference in their entireties.

FIELD

The present disclosure is directed to systems and methods of automatedcomputer analysis. Specifically, automated systems and methods ofrouting incoming communication and providing guidance regarding incomingcommunications based on continuous feedback received by the system.

BACKGROUND

In modern high-volume customer engagement centers (CEC), there are anumber of ways communication between a customer service representative(CSR) and a customer can take place. For example, communication in a CECcan take place over the telephone, through email, and text chat, amongother known communication methods. Further it is often the case that acustomer contact or communication requires a wide variety ofcommunication protocols and resources. It is extremely important for aCEC system to provide efficient and proper routing of incoming customercommunication. It is just as important for a CEC system to provide CSRswith accurate and helpful guidance for servicing the incoming customercommunications routed to them.

There are a number of methods and systems designed to assist in therouting of incoming customer communications and provide recommendationsto CSRs to assist in servicing the incoming customer communication.Typically, these methods and systems receive initial data regarding theincoming communication and determine how to route/providerecommendations based on that initial data and a set of staticgeneralized rules. Further, these known systems and methods do notutilize the different types of feedback a company may receive from whenmaking a routing/recommendation determination.

For example, the system may have static rules that indicate certaintypes of communications, such as phone calls about changing services,are to be directed to one of particular group of CSRs. Those CSRs mightalso be assigned to handle other types of interactions as well. When anincoming communication occurs, the system will route all phone callsabout changing services to that set of CSRs. The system might also evenhave a general ranking of each CSR and take the general ranking intoaccount when routing or determining guidance to recommend. However,typical systems are not robust enough to determine that one of the CSRsassigned to the group for handling phone calls about changing serviceshas been getting poor feedback on those particular types of calls.Further, due to the nature of static rules that are manually updatedwhen changes are needed, even if the system is robust enough, the rulechange would need to be implemented manually.

In another example, a CSR assigned to communicate with a high-valuecustomer may mistakenly use an old communication protocol still storedin the CEC computer system as opposed to a new protocol designed toaccount for the customer's new preferences. By the time the CSR realizestheir mistake, the customer relationship may be damaged.

There is an unmet need in the art for a system capable of automaticallyanalyzing customer service interactions including feedback provided bycustomer surveys, feedback provided by customer sentiment, and feedbackprovided by the results of decisions made by CSRs and automatedlyupdating routing rules and recommendation rules based on the analysis.

SUMMARY

An embodiment of the present application includes a method for routingbased on customer sentiment. Incoming communication from outside a CECsystem from a customer is received and analyzed using a metadataanalytics service (MAS). The incoming communication is designated aninitial communication. An initial communication is a communication thathas not been previously assigned to a CSR. The analysis determines aclient sentiment of the incoming communication. Based on the analysis ofthe MAS software module, the MAS software module generates communicationmetadata for the incoming communication, which the MAS passes, alongwith the incoming communication to a routing analysis engine (RAE). Thecommunication metadata includes at least sentiment data. The RAE alsoreceives a set of routing analytics rules (RAR) and performs an analysisof the communication metadata using a RAE software module based on thesentiment data and RAR. The RAE and determines one of a CSR, CSR group,a queue, or a queue group to receive the incoming communication. The RAEpasses the incoming communication to the assigned CSR. The RAE thenchanges the designation of the incoming communication to monitoredcommunication and passes the incoming communication, the CSRdesignation, and the communication metadata for the incomingcommunication to the to a rules analytics service (RAS). A monitoredcommunication is one that has been routed to a CSR. The RAS preforms ananalysis of the RAR using a RAS software module based on the sentimentdata and CSR designation. The RAS updates the set of RAR based on theanalysis of the RAS software module.

An embodiment of the present application includes a method of guidancebased on customer sentiment. Incoming communication from outside a CECsystem from a customer is received and analyzed using a metadataanalytics service (MAS). The incoming communication is a monitoredcommunication. A monitored communication is an incoming communicationthat is part of an ongoing customer interaction that has already beenrouted and has a set of stored data associated with it. The analysisdetermines a client sentiment of the incoming communication. Based onthe analysis of the MAS software module, the MAS software modulegenerates communication metadata for the incoming communication, whichthe MAS passes, along with the incoming communication and stored data toa guidance analysis engine (GAE). The communication metadata includes atleast sentiment data for the incoming communication. The GAE receives aset of guidance analytics rules (GAR). The GAE performs an analysis ofthe incoming communication and the communication metadata using a GAEsoftware module based on the sentiment data and GAR. The GAE assigns atleast one of a guidance to the incoming communication based on theanalysis of the GAE software module and displays the guidance on a CECdesktop. The GAE passes the guidance, the incoming communication, thestored data, and the communication metadata to a RAS. The RAS receives aCSR action associated with the incoming communication. The RAS preformsan analysis of the GAR using a RAS software module based on thesentiment data, incoming communication, the communication metadata andthe CSR action. The RAS updates the set of GAR based on the analysis ofthe RAS software module.

An embodiment of the present application includes a system for routingand guidance of incoming communications based on client sentiment. Thesystem includes a processor and a non-transitory computer readablemedium programmed with computer readable code. Upon execution of thecomputer readable medium, the processor receives an incomingcommunication from outside a customer engagement center (CEC) system.The incoming communication is designated as an initial communication oras a monitored communication. An initial communication is acommunication that has not been previously assigned to a CSR. Amonitored communication is an incoming communication that is part of anongoing customer interaction that has already been routed and has a setof stored data associated with it. A MAS unit causes the processor toperform an analysis of the incoming communication using a metadataanalytics service (MAS) software module on a MAS unit, wherein theanalysis includes a determination of a customer sentiment. Based on theanalysis, the MAS generates communication metadata for the incomingcommunication. The metadata includes at least sentiment data based onthe customer sentiment determination. If the incoming communication isdesignated initial communication, the MAS passes the incomingcommunication and the communication metadata to a routing analysisengine (RAE). The RAE causes the processor to receive a set of routinganalytics rules (RAR) at the RAE and performs an analysis of theincoming communication using a RAE software module based on thesentiment data and the set of RAR. Based on the analysis of the RAE theprocessor designates one of a customer service representative (CSR), CSRgroup, a queue, or a queue group to receive the incoming communicationand the incoming communication designation is changed to a monitoredcommunication. The incoming communication is passed to the CSR, CSRgroup, queue, or queue group designated to receive the incomingcommunication. Additionally, the incoming communication, thecommunication metadata, and the CSR designation is passed to a rulesanalytics service (RAS) software module. The RAS causes the processor toperform an analysis of the RAR using the RAS software module based onthe sentiment data and the CSR designation, and update the set of RARbased on the analysis of the RAS software module. If the incomingcommunication is designated monitored communication, the MAS passes theincoming communication, the stored data, and the communication metadatato a guidance analysis engine (GAE). The GAE causes the processor toreceive a set of guidance analytics rules (GAR) and performs an analysisof the communication metadata using a GAE software module based on thesentiment data and the set of GAR. The processor assigns a guidancebased on the analysis of the GAE software module and displays theguidance on a CSR desktop. The incoming communication, the communicationmetadata, the guidance, and the stored data are passed to to the RASsoftware module. The RAS receives a CSR action associated with theincoming communication, performs an analysis of the GAR using the RASsoftware module based on the sentiment data and the CSR action, andupdates the set of GAR based on the analysis of the RAS software module.

An embodiment of the present application includes a method for routingbased on customer survey feedback. Incoming communication from outside aCEC system from a customer is received and analyzed using a metadataanalytics service (MAS). The incoming communication is an initialcommunication. An initial communication is a communication that has notbeen routed to a CSR. Based on the analysis of the MAS software module,the MAS software module generates communication metadata for theincoming communication, which the MAS passes, along with the incomingcommunication to a routing analysis engine (RAE). The RAE receives a setof RAR. The RAE performs an analysis of the incoming communication andthe communication metadata using a RAE software module based on the RAR,determines one of a CSR, CSR group, a queue, or a queue group to receivethe incoming communication, and sends the incoming communication to thedesignated CSR. The RAE then changes the designation of the incomingcommunication to monitored communication and passes the incomingcommunication, the CSR designation, and the communication metadata forthe incoming communication to the to a rules analytics service (RAS).The RAS stores the incoming communication, the communication metadata,and the CSR designation. Optionally, the RAS may send a customer surveyto the customer or the RAS may instruct the system to send a customersurvey to the customer. The customer survey is associated with theincoming communication. When the RAS receives customer survey dataassociated with the incoming communication, the RAS preforms an analysisof the RAR using a RAS software module based on the customer survey dataand CSR designation. The RAS updates the set of RAR based on theanalysis of the RAS software module.

An embodiment of the present application includes a method of guidancebased on customer survey feedback. Incoming communication from outside aCEC system from a customer is received and analyzed using a metadataanalytics service (MAS). The incoming communication is a monitoredcommunication. A monitored communication is an incoming communicationthat is part of an ongoing customer interaction that has already beenrouted and has stored data associated with it. Based on the analysis ofthe MAS software module, the MAS software module generates communicationmetadata for the incoming communication, which the MAS passes, alongwith the incoming communication and stored data to a guidance analysisengine (GAE). The GAE receives a set of GAR. The GAE performs ananalysis of the incoming communication using a GAE software module basedon the communication metadata and the GAR. The GAE assigns at least oneguidance to the incoming communication based on the analysis of the GAEsoftware module and displays the guidance on a CEC desktop. The GAEpasses the guidance, the incoming communication, the stored data, andthe communication metadata to a RAS. The RAS receives a CSR actionassociated with the incoming communication. The RAS stores the incomingcommunication, the communication metadata, the guidance, the storeddata, and the CSR action to RAS storage. The RAS may send a customersurvey to the customer. The customer survey is associated with theincoming communication. When the RAS receives customer survey dataassociated with the incoming communication, the RAS preforms an analysisof the GAR using a RAS software module based on the customer surveydata, the communication metadata, and CSR action. The RAS updates theset of GAR based on the analysis of the RAS software module.

An embodiment of the present application includes a system for routingand guidance of incoming communications based on customer surveyfeedback. The system includes a processor and a non-transitory computerreadable medium programmed with computer readable code. Upon executionof the computer readable medium, the processor receives an incomingcommunication from outside a customer engagement center (CEC) system.The incoming communication is designated as an initial communication oras a monitored communication. An initial communication is acommunication that has not been previously assigned to a CSR. Amonitored communication is an incoming communication that is part of anongoing customer interaction that has already been routed and has a setof stored data associated with it. A MAS unit causes the processor toperform an analysis of the incoming communication using a metadataanalytics service (MAS) software module on a MAS unit. Based on theanalysis, the MAS generates communication metadata for the incomingcommunication. If the incoming communication is designated initialcommunication, the MAS passes the incoming communication and thecommunication metadata to a routing analysis engine (RAE). The RAEcauses the processor to receive a set of routing analytics rules (RAR)at the RAE and performs an analysis of the incoming communication usinga RAE software module based on the communication metadata and the set ofRAR. Based on the analysis of the RAE the processor designates one of acustomer service representative (CSR), CSR group, a queue, or a queuegroup to receive the incoming communication and the incomingcommunication designation is changed to a monitored communication. Theincoming communication is passed to the CSR, CSR group, queue, or queuegroup designated to receive the incoming communication. The RAS causesthe processor to store the incoming communication, the communicationmetadata, and the CSR designation. The RAS may send a customer survey tothe customer. The customer survey is associated with the incomingcommunication. When the customer survey data associated with theincoming communication is received, the RAS causes the processor toperform an analysis of the RAR using a RAS software module based on thecustomer survey data and CSR designation and update the set of RAR basedon the analysis of the RAS software module. If the incomingcommunication is designated monitored communication, the MAS passes theincoming communication, the stored data, and the communication metadatato a guidance analysis engine (GAE). The GAE causes the processor toreceive a set of guidance analytics rules (GAR) and performs an analysisof the incoming communication using a GAE software module based on thecommunication metadata, stored data, and the set of GAR. The processorassigns a guidance based on the analysis of the GAE software module anddisplays the guidance on a CSR desktop. The incoming communication, thecommunication metadata, the guidance, and the stored data are passed tothe RAS software module. The RAS receives a CSR action associated withthe incoming communication. The RAS causes the processor to store theincoming communication, the communication metadata, the guidance, thestored data, and the CSR action to RAS storage. The RAS may send acustomer survey to the customer. The customer survey is associated withthe incoming communication. When the customer survey data associatedwith the incoming communication is received, the RAS causes theprocessor to perform an analysis of the GAR using a RAS software modulebased on the customer survey data, the communication metadata, and CSRaction and update the set of GAR based on the analysis of the RASsoftware module.

An embodiment of the present application includes a method of guidancebased on CSR action weighted by CSR skill level for the skill requiredby the incoming communication. Incoming communication from outside a CECsystem from a customer is received and analyzed using a metadataanalytics service (MAS), wherein the analysis includes a determinationof a conversation type for the incoming communication. The incomingcommunication is a monitored communication. A monitored communication isan incoming communication that is part of an ongoing customerinteraction that has already been routed. A monitored communicationincludes stored data associated with the incoming communication. Thestored data includes at least a CSR designation and a set of CSR skilllevels associated with the CSR designation. Based on the analysis of theMAS software module, the MAS software module generates communicationmetadata for the incoming communication, which the MAS passes, alongwith the incoming communication and stored data to a guidance analysisengine (GAE). The communication metadata includes skill type for theincoming communication. The GAE receives a set of GAR. The GAE performsan analysis of the incoming communication using a GAE software modulebased on the skill type and the GAR. The GAE assigns at least one of aguidance to the incoming communication based on the analysis of the GAEsoftware module and displays the guidance on a CEC desktop. The GAEpasses the guidance, the incoming communication, the stored data, andthe communication metadata to a RAS. The RAS receives a CSR actionassociated with the incoming communication. The RAS preforms an analysisof GAR using a RAS software module based on the CSR skill levelassociated with the skill type for the incoming communication, theincoming communication, the communication metadata and the CSR action.The RAS updates the set of GAR based on the analysis of the RAS softwaremodule.

An embodiment of the present application includes a system for guidanceof incoming communications based on CSR action weighted by CSR skilllevel for the skill required by the incoming communication. The systemincludes a processor and a non-transitory computer readable mediumprogrammed with computer readable code. Upon execution of the computerreadable medium, the processor receives an incoming communication fromoutside a customer engagement center (CEC) system. The incomingcommunication is a monitored communication. A monitored communication isan incoming communication that is part of an ongoing customerinteraction that has already been routed and has a set of stored dataassociated with it. The set of stored data includes a CSR designationfor the incoming communication and a set of CSR skill levels associatedwith the CRS designation. A MAS unit causes the processor to perform ananalysis of the incoming communication using a metadata analyticsservice (MAS) software module on a MAS unit, wherein the analysisincludes a determination of a conversation type for the incomingcommunication. Based on the analysis, the MAS generates communicationmetadata for the incoming communication. The metadata includes at leasta skill type based on the conversation type determination. The MASpasses the incoming communication, the stored data, and thecommunication metadata to a guidance analysis engine (GAE). The GAEcauses the processor to receive a set of guidance analytics rules (GAR)and performs an analysis of the communication metadata using a GAEsoftware module based on the skill type and the set of GAR. Theprocessor assigns a guidance based on the analysis of the GAE softwaremodule and displays the guidance on a CSR desktop. The incomingcommunication, the communication metadata, the guidance, and the storeddata are passed to the RAS software module. The RAS receives a CSRaction associated with the incoming communication, performs an analysisof GAR using a RAS software module based on the CSR skill levelassociated with the skill type for the incoming communication, theincoming communication, the communication metadata and the CSR action,and updates the set of GAR based on the analysis of the RAS softwaremodule.

In embodiments that provide for routing and guidance based, as least inpart, on prior actions taken by CSRs, the routing decisions and guidancewill be weighted by the CSRs skill level as it pertains to the actiontaken. For example, an employee who processes complaints may have aspecific skill associated with them in the CEC system's model for thattask. Each employee in the system will have multiple skills modelledagainst them, representing the variety of different tasks they willcarry out as part of their job. For example, if an interaction comesinto the CEC that relates to upgrades and there is a CSR with a highskill level pertaining to upgrades the system may route the interactionto that CSR even though that CSR may have a longer wait queue thananother CSR with a lower skill rank in that type of task.

Some systems also enhance such models to include skill levels indicatingthe degree of experience that an employee has in a specific area. Forexample, an employee may have handled a certain type of work for anumber of years throughout their career. That employee will likely bemore skilled at handling that type of work than another employee who hasless experience in the same area. In such a situation, the GAR shouldfavor the insights it gains from the former employee more so than thelatter. Such insights will therefore carry more weight in thedecision-making process. Further, it should be understood that if theCSR handling the interaction has a higher skill ranking than previousCSR's the system may not provide any recommendation to that CSR for thatinteraction. Further, it should be understood that the system may alsoincorporate procedures for lowing the likelihood of the recommendationof actions taken by CSRs with low skill rankings. Further, it should beunderstood that a CSRs skill level for a particular type of skill maychange over time. The skill level weight should be the CSRs currentskill level at the time the action was taken.

The objects and advantages will appear more fully from the followingdetailed description made in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWING(S)

FIG. 1 depicts an exemplary embodiment of a CEC system for automatedlyrouting incoming customer interactions and recommending guidance to CSRsin handling customer interactions based on incorporating customer surveyfeedback, customer sentiment feedback, and feedback based on the resultsof CSR recommendations.

FIGS. 2 a and 2 b depict a flowchart of an exemplary embodiment of amethod of automatedly routing incoming customer interactions andrecommending guidance to CSRs in handling customer interactions based onincorporating customer survey feedback, customer sentiment feedback, andfeedback based on the results of CSR recommendations.

FIG. 3 depicts an exemplary embodiment of a system for automatedlyrouting incoming customer interactions and recommending guidance to CSRsin handling customer interactions based on incorporating customer surveyfeedback, customer sentiment feedback, and feedback based on the resultsof CSR recommendations.

DETAILED DESCRIPTION OF THE DRAWING(S)

In the present description, certain terms have been used for brevity,clearness and understanding. No unnecessary limitations are to beapplied therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes only and are intended to bebroadly construed. The different systems and methods described hereinmay be used alone or in combination with other systems and methods.Various equivalents, alternatives and modifications are possible withinthe scope of the appended claims. Each limitation in the appended claimsis intended to invoke interpretation under 35 U.S.C. § 112, sixthparagraph, only if the terms “means for” or “step for” are explicitlyrecited in the respective limitation.

CEC systems allow CSRs to engage with customers in a controlled manner.By providing organized and integrated computer-based customer serviceresources and efficient intelligent routing of incoming customercommunication through incorporating customer survey feedback, customersentiment feedback, and feedback on CSR decisions regardingrecommendations, the CEC system can allow an organization to achieveseveral key benefits. These benefits will be increased through thedynamic automated updating of analysis rules. First, the system willensure intelligent real-time routing decisions. Second, the CEC system'sadaptive nature allows it to adjust to new protocols, individual CSRsuccessful habits and preferences, and customers' preferences. Third,the CEC system's interlinked and adaptive nature allows it to adjust towide-scale organizational habits and preferences developed over time,and to provide accurate feedback for evaluation of the use of availableresources.

In embodiments, it is desirable for the system to update the RAR and GARused by the CEC system to utilize analyzed data. This permits the systemto adapt to changes in customer feedback in the form of customer surveysand customer sentiment. It also allows the system to weigh the decisionsmade by CSRs based on their changing skill levels for a particularskill. In an embodiment, it is desirable to update the analytics rules(RAR and GAR) based on the customer's sentiment in response to a CSRaction. In another embodiment, it is desirable to update the analyticsrules (RAR and GAR) based on the customer's feedback survey. In anotherembodiment, it is desirable to update the analytics rules (RAR and GAR)based on the CSR action taken balanced by the CSR skill. This may beaccomplished in real-time or in batches to train the system. In anotherembodiment, it is desirable to update the analytics rules based on inputfrom a CEC desktop.

In embodiments, it is desirable for the system to analyze incomingcustomer communication to create metadata and then route the incomingcommunication according to dynamically updated RARs. This allows thesystem to intelligently route incoming communication. In embodiments, itis desirable for the system to monitor and analyze ongoing customercommunication to create and pull metadata and stored data and then makeguidance recommendations according to dynamically updated GARs. Thisallows the system to intelligently make recommendations based on updatedcurrent information about the communication, the CSR, and the customer.Guidance provided by the system can include, but is not limited to arecommendation on what script(s) to read, a recommendation to providethe client with an article or to consult an article, a recommendation onproducts to offer to the client, a notification that the client is adifficult client or has particular requirements, and/or a recommendationas to which CEC tool(s) the CSR should use for the current interaction.

FIG. 1 depicts an exemplary embodiment of CEC system 100 for automatedlyrouting incoming customer interactions and recommending guidance to CSRsin handling customer interactions based on incorporating customer surveyfeedback, customer sentiment feedback, and feedback based on the resultsof CSR recommendations.

CEC system 100 includes a MAS unit 110 having a MAS software module 111and an optional MAS storage 112. MAS unit 110 may be a processor or acombination of a processing system and a storage system. MAS unit 110receives incoming communications, which are either initialcommunications 120 or monitored communications 121 from outside the CECsystem 100. An initial communication is a communication that is not partof a monitored communication and has not been previously routed to aCSR. A monitored communication is an incoming communication that is partof an ongoing customer interaction that has already been routed and hasa set of stored data 125 associated with it. The set of stored data 125may include for example, metadata from prior incoming communicationsassociated with the current incoming monitored communication the CSRdesignation for the incoming communication, a set of CSR skill levelsassociated with the CRS designation, etc. The MAS unit 110 analyzes theinitial communications 120 and the monitored communications 121 usingMAS software module 111 to generate communication metadata 122.Optionally, MAS unit 110 may also pass a copy of the initialcommunications 120, monitored communications 121, the set of stored data125, and/or communication metadata 122 to internal or external MASstorage 112 for permanent or temporary storage.

Incoming communications, both initial communications 120 and monitoredcommunications 121, may be audio communication such as a telephone call,a voice message, a video chat, or any other type of audio communication,written communication such as an email, an online posting, a directmessage from a customer, or any other written communication. Theanalysis of initial communications 120 and monitored communications 121may include a variety of techniques such as speech analytics or textualanalytics of audio communications or textual communications betweencustomers and CSRs. The initial communications 120 and monitoredcommunications 121 can further include a wide variety of channels ofdata. These channels can include audio or textual transcripts of phonecalls or web data, but can also include more discretely occurring eventssuch as social medial posts, purchases, returns, or warranty claims. Theanalysis may include determining the client sentiment for the incomingcommunication and determining the conversation type the incomingcommunication involves, for example returns or ordering.

Communication metadata 122 may include, but is not limited to, sentimentincluding tone, word choice of the customer, intent and/or meaning ofthe incoming communication; presence of threats; the skill typenecessary to assist the incoming communication, and/or a list ofbusiness entities referenced in the incoming communication 120 and 121.For example, the list of business entities referenced in thecommunication 120 and 121 may include at least one of a policy, anaccount, a customer, an involved or associated third party, and/or otherparties identified in the communication 120 and 121. In certainembodiments, the intent of the correspondence is expressed as a list ofaction points ordered by important or urgency.

CEC system 100 also includes a RAE 130 having a RAE software module 131.RAE 130 is configured to constantly receive and analyze routing datawithin the CEC system 100. Analysis may be a real-time analysis ofstreaming data or batch analysis of data which is used to train RAE 130.RAE 130 may be a processor or a combination of a processing system and astorage system. RAE 130 receives RAR 132 from rules analysis system(RAS) 150. RAE 130 receives initial communication 120 with communicationmetadata 122 from MAS unit 110 and analyzes it using RAE software module131 based on RAR 132 to route initial communication 120. Optionally, RAE130 may also route communication metadata 122 to the same destination ora different destination from initial communication 120. Optionally, RAE130 may also pass a copy of initial communication 120 and/orcommunication metadata 122 to internal or external RAE storage 133 forpermanent or temporary storage.

Depending on communication metadata 122 and RAR 132, RAE 130 may routeinitial communication 120 to a specific CSR, a group of CSRs, a specificqueue, or a group of queues. By way of non-limiting example, if initialcommunication 120 is from a specific customer that has reported anunsatisfactory experience with a particular CSR on a feedback survey,that initial communication may be routed to a different CSR even if thatCSR has the highest-ranking skill for the particular initialcommunication. By way of another non-limiting example, if thecommunication metadata 122 indicates that the sentiment of the initialcommunication 120 is unhappy, the initial communication may betransferred to a CSR who has received positive feedback on surveys fordealing with unhappy customers, rather than to a CSR skilled in theparticular type of initial communication 120. The RAE routes thecommunication to the designated CSR. Optionally, the RAE may display theinitial communication 120, and/or the communication metadata to a CECdesktop 160.

CSRs may be identified or grouped by level of authority or skill, skillset, product or service line, department, assigned customers oraccounts, prior customer interactions, any other quality, or anycombination of qualities. Queues and queue groups may be associated witha level of urgency or importance, with one or more specific issues,types of issue, products, services, product lines, service lines,customers, accounts, departments, or groups of departments, any otherquality, or any combination of qualities. CSR groups and queue groupsmay be predetermined or created and updated dynamically to fit currentor anticipated needs. By way of non-limiting example, a predeterminedCSR group may include all CSRs of a given authority level. By way offurther non-limiting example, a dynamic queue group may include queuesfor a large, very important customer and a specific time-limited issue;this queue group may be dissolved after the time to resolve the issuehas expired. By way of further non-limiting example, a dynamic queuegroup may exclude a specific CSR based on the specific customer that isparty to the communication and prior survey feedback provided by thatcustomer.

CEC system 100 also includes a GAE 140 having a GAE software module 141.GAE 140 is configured to constantly receive and analyze guidance datawithin the CEC system 100. Analysis may be a real-time analysis ofstreaming data or batch analysis of data which is used to train GAE 140.GAE 140 may be a processor or a combination of a processing system and astorage system. GAE 140 receives GAR 142 from RAS 150. GAE 140 alsoreceives monitored communication 121 with communication metadata 122 andstored data 125 from MAS unit 110. GAE 140 analyzes the monitoredcommunication 121, the communication metadata 122, and the stored data125 using GAE software module 141 based on GAR 142 and generatesguidance 144 to provide to the CSR for the communication. Optionally,GAE 140 may also pass a copy of monitored communication 121,communication metadata 122, stored data 125, and/or guidance 144 tointernal or external GAE storage 133 for permanent or temporary storage.

Depending on communication metadata 122, stored data 125, and GAR 142,GAE 140 may recommend guidance to a CSR based on the customer'ssentiment feedback, the survey feedback for the specific customer, thesurvey feedback for the specific CSR, and/or actions taken by other CSRsbased on CSR skill ranking. By way of non-limiting example, if monitoredcommunication 121 is from a specific customer that has filled out afeedback survey indicating he/she never wants to be asked to upgrade,GAE 140 may provide guidance to the CSR indicating that information. Byway of another non-limiting example, if the communication metadata 122contains sentiment data indicating that the customer is unhappy, GAE 140may provide guidance to the CSR directing the CSR to take steps thatother CSRs with high skill rankings in dealing with unhappy customershave done and resulted in the customer's sentiment changing to happy.Customer surveys may be sent out to the customer at the time a call isrouted to a CSR, after the communication has been terminated, or at anyother time during the interaction or after the interaction is complete.Every customer survey is associated with an incoming communication suchthat the survey may be tailored to the incoming communication and suchthat the survey data received from the customer when the survey iscompleted can be associated with the same incoming communication.Customer feedback surveys may be sent and received through all knownmethods for sending and receiving feedback surveys, including, but notlimited to physically through US mail or other postal methods, digitallythrough email, text chat, or a survey system. Customer survey data maybe received at the CEC through all of the above methods and anotherother methods known for receiving feedback. Based on the manner thesurvey data is received by the CEC, the data will automatedly be enteredinto the system and associated with the incoming communication relatedto the survey or may need to be manually entered into the system by aCSR or other employee of the CEC.

CEC system 100 also includes at least one CSR desktop 160 used by theCSR for viewing initial communication 120, monitored communication 121,guidance 144, and optionally communication metadata 122. CEC desktop 160may also receive input for updating RAR 132, GAR 142, and receives inputof the action taken by the CSR 146. CEC desktop 160 may also receiveinput for updating survey feedback 148.

CEC system 100 also includes a RAS 150 having a RAS software module 151.RAS 150 is configured to constantly receive and analyze data andautomatedly update RAR 132 and GAR 142 within CEC system 100. Analysismay be a real-time analysis of streaming data or batch analysis of datafor training the RAS 150. RAS 150 includes at least one set of RAR 132and at least one set of GAR 142 used to analyze data. RAS 150 may be aprocessor or a combination of a processing system and a storage system.RAS 150 receives initial communications 120, communication metadata 122,and CSR designation 123 from RAE 130. RAS 150 also receives monitoredcommunication 121, communication metadata 122, stored data 125, andguidance 144 from GAE 140. RAS 150 receives a CSR action 146 and surveyfeedback 148 from CSR desktop 160. RAS 150 analyzes the data receivedand updates RAR 132 and GAR 142 according to the analysis. In oneembodiment, the RAS is configured to analyze the CSR action and theguidance in conjunction with the CSR skill ranking. In anotherembodiment, the RAS software module is configured to analyze thesentiment metadata in conjunction with the CSR action. In an embodiment,the RAS software module is configured to analyze the feedback surveydata in conjunction with the CSR action.

RAR 132 includes rules conditioned on metadata 122 including sentimentand skill type, CSR or CSR group availability, CSR or CSR groupworkload, CSR or CSR group skills, CSR skill ranking, and surveyfeedback 148. RAR 132 may be dynamically updated by RAS 150 and/or auser or third party utilizing CEC desktop 160. Updates may be manual orautomatic. Automatic updates to RAR 132 may be triggered by meetingcertain criteria within RAR 132 of RAS 150, or may occur atpredetermined intervals, or may occur in real-time. RAR 132 may besoftware programs or separate files executed by a software program.

GAR 142 includes rules conditioned on metadata 122 including sentimentand skill type, CSR skills, CSR skill ranking, guidance 144, CSR action146, and survey feedback 148. GAR 142 may be dynamically updated by RAS150 and/or a user or third party utilizing CEC desktop 160. Updates maybe manual or automatic. Automatic updates to GAR 142 may be triggered bymeeting certain criteria within GAR 142 of RAS 150, or may occur atpredetermined intervals, or may occur in real-time. GAR 142 may besoftware programs or separate files executed by a software program.

A CSRs skill level, ranking, and skill set may change over time based onsurvey feedback 148, customer sentiment feedback 122, and other sourcesof feedback. The changes to a CSRs still level, ranking, and skill setmay be updated manually or automatically. Automatic updates to CSR skillmay be triggered by certain criteria within RAR 132 of RAS 150, certaincriteria within GAR 142 of RAS 150, or may occur at predeterminedintervals, or may occur in real-time based on customer survey feedbackand customer sentiment feedback.

FIGS. 2 a and 2 b depict a flowchart of an exemplary embodiment ofmethod 200 for automatedly routing incoming customer interactions andrecommending guidance to CSRs in handling customer interactions based onincorporating customer survey feedback, customer sentiment feedback, andfeedback based on the results of CSR recommendations.

In step 202, the MAS unit receives a communication from outside the CECsystem. The communication may be an initial communication that needs tobe routed before guidance is provided or the communication may be partof an ongoing monitored communication that has already been routed.Monitored communications are also associated with stored data that theMAS unit also receives. The communications may be multi-sided, such as,but not limited to, a three-way telephone call or an instant messageexchange between a CSR and a customer, or one-sided, such as, but notlimited to, an email composed by a customer. The stored data mayinclude, but is not limited to a CSR designation, a set of CSR skilllevels for the designated CSR, previous metadata from priorcommunications associated with the monitored communication.

In step 204, the MAS unit performs an analysis of the incomingcommunication using the MAS software module. The analysis may evaluatethe communication message content, existing stored data in the case ofmonitored communications, existing metadata including whether thecommunication is an initial communication or a monitored communication,header data, and attachments. In one embodiment, the MAS unit analyzesthe communication for sentiment. In other embodiments, the MAS analysisdoes not include sentiment analysis. In some embodiments, the MAS unitanalyzes the communication for conversation type. In other embodiments,the MAS analysis does not include conversation type analysis.

In step 206, the MAS unit generates communication metadata for thecommunication based on the analysis of step 204. In one embodiment, themetadata will include sentiment metadata. In other embodiments,sentiment metadata will not be included. In other embodiments themetadata will include skill type required for the communication. Inother embodiments, skill type metadata will not be included. It shouldbe understood that the metadata is not limited to the above metadata andmay include any other useful metadata.

If the communication is an initial communication, in step 208, the MASunit will pass the communication and the associated communicationmetadata to a RAE. Optionally, the MAS unit may also pass thecommunication and/or associated metadata to MAS storage.

In step 210, the RAE receives the current set of RARs from the RAS.

In step 212, the RAS performs an analysis of the initial communicationusing a RAE software module based on the communication metadata andRARs.

In step 214, the RAE designates a CSR, CSR group, a queue, or a queuegroup to receive the communication based on the analysis of step 212.

In step 216 the RAE changes the communication from that of an initialcommunication to a monitored communication.

In step 218, the RAE passes the communication to the designated CSR, CSRgroup, queue, or queue group from step 214.

In optional step 220, the RAE stores the incoming communication, thecommunication metadata, and/or the CSR designation to RAE storage.

In optional step 222, the RAE displays the incoming communication,and/or the communication metadata to the designated CSR, CSR group,queue, or queue group from step 214 and/or to another party on a CECdesktop. This step may occur simultaneously with step 216.

In step 224, the RAE passes the communication, the communicationmetadata, and the CSR designation to the RAS.

In optional step 226, the RAS stores the communication, thecommunication metadata, and the CSR designation to the RAS storage.

In optional step 228, the RAS sends a feedback survey to the client. Thefeedback survey may be an email to which the customer responds, aweb-based fill-in form, a written survey delivered by US Mail, or anyother type of survey, including third-party survey services. Whateverform the feedback survey takes, it is configured to be associated withthe communication, communication metadata, guidance, and CSR action fromthe specific customer service interaction.

In optional step 230, the RAS receives feedback data. In someembodiments the feedback data is survey data associated with an incomingcommunication. In other embodiments, the feedback data does not includesurvey data. The feedback data is not limited in its scope. In someembodiments, the feedback data is part of the metadata generated in step206.

In step 232, the RAS analyzes the RAR based on feedback data and the CSRdesignation. In an embodiment the feedback data is survey dataassociated with an incoming communication. In another embodiment thefeedback data is the sentiment data generated as metadata in step 206.

In step 234, the RAS updates the RAR based on the analysis from step232. At step A, RAE then passes the incoming communication (now amonitored communication), along with the metadata, and CSR designation,collectively stored data, back to step 202 for guidance processing. Thestored data may also include any other data associated with themonitored communication including any received feedback data.

If the communication received at step 202 is not an initialcommunication and has already been routed, steps 208-234 are be skipped.At step 236, the GAE receives the monitored communication, the storeddata, and the associated communication metadata from the MAS unit.Optionally, the MAS unit may also pass the communication and/orassociated metadata to MAS storage.

In step 238, the GAE receives the current set of GARs from the RAS.

In step 240, the GAE performs an analysis of the communication using aRAE software module based on the, stored data, the communicationmetadata, and the GARs. In an embodiment the analysis is based off ofthe GARs and the sentiment data. In another embodiment the analysis isbased off of the GARs and the determined skill type of thecommunication. These are merely examples of the basis for the analysisand are not to be considered limiting.

In step 242, the GAR assigns guidance based on the analysis of step 240.

In step 244, the GAR displays the guidance from step 242 on the CECdesktop.

In optional step 246 the GAR displays the communication and/or thecommunication metadata on the CEC desktop. It should be understood thatthis step may occur simultaneously with step 244.

In optional step 248, the GAR may also pass the communication, thecommunication metadata, the stored data, and/or the guidance to GARstorage. It should be understood that this step may occur simultaneouslywith steps 244 and 246.

In step 250, the GAR passes the communication, the communicationmetadata, the stored data, and the guidance to a RAS.

In step 252, the RAS receives the CSR action from the CEC desktop.

In optional step 254, the RAS stores the communication, thecommunication metadata, the stored data, the guidance, and/or the CSRaction to a RAS storage.

In optional step 256, the RAS sends a feedback survey to the client. Thefeedback survey may be an email to which the customer responds, aweb-based fill-in form, a written survey delivered by US Mail, or anyother type of survey, including third-party survey services. Whateverform the feedback survey takes, it is configured to be associated withthe communication, communication metadata, guidance, and CSR action fromthe specific customer service interaction.

In optional step 258, the RAS receives feedback data. In someembodiments the feedback data is survey data associated with an incomingcommunication. In other embodiments, the feedback data does not includesurvey data. The feedback data is not limited in its scope. In someembodiments, the feedback data is part of the metadata generated in step206. In yet other embodiments, the feedback data is part of the storeddata associated with the monitored communication.

In step 260, the RAS analyzes the GAR based on feedback data and the CSRaction. In an embodiment the feedback data is survey data associatedwith an incoming communication. In another embodiment the feedback datais the sentiment data generated as metadata in step 206. In yet anotherembodiment, the feedback data is the skill level of the skill type forthe designated CSR. In an embodiment, the RAS software module isconfigured to analyze the feedback survey data in conjunction with theCSR action. In a non-limiting example, if the feedback survey indicatesdissatisfaction with the interaction, the RAS may update the RAR to keepthat customer from being routed to the particular CSR the customer wasdissatisfied with. In the same non-limiting example, the RAS may updatethe GAR to keep the CSR action associated with the interaction frombeing provided as guidance in similar interactions.

In step 262, the RAS updates the GAR based on the analysis from step260.

In optional step 264, the RAS receives an update to the GAR and/or RARfrom the CSR or another party. In various embodiments, this step mayoccur before or after any other step in method 200.

In optional step 266, for training, the RAS receives a batch ofpreviously stored interaction data including communication metadatawhich may include sentiment data or skill data, associated CSR actions,associated guidance, stored data, CSR designation, associated feedbacksurvey data, and associated communication.

In optional step 268, the RAS analyzes the received data. In oneembodiment, the RAS software module is configured to analyze the CSRaction and the guidance in conjunction with the CSR skill ranking. Inanother embodiment, the RAS software module is configured to analyze thesentiment metadata in conjunction with the CSR action. In an embodiment,the RAS software module is configured to analyze the feedback surveydata in conjunction with the CSR action. In another embodiment, the RASsoftware module is configured to analyze the sentiment metadata inconjunction with the CSR designation. In an embodiment, the RAS softwaremodule is configured to analyze the feedback survey data in conjunctionwith the CSR designation.

In optional step 270, the RAS updates the RAR based on the analysis fromstep 268.

In optional step 272, the RAS updates the GAR based on the analysis fromstep 268. It should be understood that this step could occur before orsimultaneously to step 270.

In various embodiments, optional steps 264-272 may occur before or afteror during any other step in method 200.

FIG. 3 depicts an exemplary embodiment of a system 300 for automatedlyrouting incoming customer interactions and recommending guidance to CSRsin handling customer interactions based on incorporating customer surveyfeedback, customer sentiment feedback, and feedback based on the resultsof CSR recommendations.

System 300 is generally a computing system that includes a processingsystem 306, a storage system 304, software 302, a communicationinterface 308, and a user interface 310. Processing system 306 loads andexecutes software 302 from the storage system 304, including a softwaremodule 320. When executed by computing system 300, software module 320directs the processing system 306 to operate as described in herein infurther detail in accordance with the method 200.

Computing system 300 includes four software modules 320 for performingthe functions of MAS software module 111, RAE software module 131, GAEsoftware module 141, and RAS software module 151. Although computingsystem 300 as depicted in FIG. 3 includes four software modules 320 inthe present example, it should be understood that one or more modulescould provide the same operation. Similarly, while the description asprovided herein refers to a computing system 300 and a processing system306, it is to be recognized that implementations of such systems can beperformed using one or more processors, which may be communicativelyconnected, and such implementations are considered to be within thescope of the description. It is also contemplated that these componentsof computing system 300 may be operating in a number of physicallocations.

The processing system 306 can comprise a microprocessor and othercircuitry that retrieves and executes software 302 from storage system304. Processing system 306 can be implemented within a single processingdevice but can also be distributed across multiple processing devices orsub-systems that cooperate in existing program instructions. Examples ofprocessing systems 306 include general purpose central processing units,application specific processors, and logic devices, as well as any othertype of processing device, combinations of processing devices, orvariations thereof.

The storage system 304 can comprise any storage media readable byprocessing system 306, and capable of storing software 302. The storagesystem 304 can include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Storage system 304 can be implemented asa single storage device but may also be implemented across multiplestorage devices or sub-systems. Storage system 304 can further includeadditional elements, such a controller capable of communicating with theprocessing system 306.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto store the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media. In someimplementations, at least a portion of the storage media may betransitory. Storage media may be internal or external to system 300.

User interface 310 can include one or more CEC desktops 160, a mouse, akeyboard, a voice input device, a touch input device for receiving agesture from a user, a motion input device for detecting non-touchgestures and other motions by a user, and other comparable input devicesand associated processing elements capable of receiving user input froma user. Output devices such as a video display or graphical display candisplay initial communication 120, monitored communication 121,communication metadata 122, guidance 144, CEC desktop 160, or anotherinterface further associated with embodiments of the system and methodas disclosed herein. Speakers, printers, haptic devices and other typesof output devices may also be included in the user interface 310. A CSRor other staff can communicate with computing system 300 through theuser interface 310 in order to view initial communication 120, monitoredcommunication 121, communication metadata 122, guidance 144, update RAR132, update GAR 142, input a CSR action 146, enter feedback data 148,enter customer input, manage an interaction, or any number of othertasks the CSR or other staff may want to complete with computing system300.

As described in further detail herein, computing system 300 receives andtransmits data through communication interface 308. In embodiments, thecommunication interface 308 operates to send and/or receive data, suchas, but not limited to, initial communication 120 and monitoredcommunication 121 to/from other devices and/or systems to whichcomputing system 300 is communicatively connected, and to receive andprocess customer input, as described in greater detail above. Thecustomer input can include initial communication 120, monitoredcommunication 121, details about a request, work order or other set ofinformation that will necessitate an interaction between the customerand the representative. Customer input may also be made directly to theCSR, as described in further detail above.

In the foregoing description, certain terms have been used for brevity,clearness, and understanding. No unnecessary limitations are to beinferred therefrom beyond the requirement of the prior art because suchterms are used for descriptive purposes and are intended to be broadlyconstrued. The different configurations, systems, and method stepsdescribed herein may be used alone or in combination with otherconfigurations, systems and method steps. It is to be expected thatvarious equivalents, alternatives and modifications are possible withinthe scope of the appended claims.

What is claimed is:
 1. A method for routing based on client sentiment,comprising: generating the client sentiment for an incomingcommunication based on an analysis of the incoming communication;selecting at least one customer service representative (CSR) designationto receive the incoming communication based on the client sentiment anda set of routing analytics rules (RAR); performing an analysis of theset of RAR based on the client sentiment and the selected at least oneCSR designation; and updating the set of RAR based on the analysis. 2.The method of claim 1, further comprising providing a customerengagement center system, the customer engagement center system isconfigured to receive incoming communications from outside of thecustomer engagement center system.
 3. The method of claim 2, furthercomprising receiving the incoming communication at the customerengagement center.
 4. The method of claim 1, wherein the clientsentiment is generated as a set of communication metadata.
 5. The methodof claim 4, wherein the selecting at least one customer servicerepresentative designation is also based on the set of communicationmetadata including the client sentiment.
 6. The method of claim 1,wherein the at least one customer service representative designation isat least one of a CSR, a CSR group, a queue, or a queue group.
 7. Themethod of claim 1, the method further comprising providing at least oneof the incoming communication or the client sentiment to the customerservice representative designation.
 8. A system for routing based onclient sentiment, the system comprising: a processor, and a memorycomprising computer readable instructions that upon execution by theprocessor causes the system to: generate the client sentiment for anincoming communication based on an analysis of the incomingcommunication; select at least one customer service representative (CSR)designation to receive the incoming communication based on the clientsentiment and a set of routing analytics rules (RAR); perform ananalysis of the set of RAR based on the client sentiment and theselected at least one CSR designation; and update the set of RAR basedon the analysis.
 9. The system of claim 8, wherein the system furthercomprises a customer engagement center system, the customer engagementcenter system is configured to receive incoming communications fromoutside of the customer engagement center system.
 10. The system ofclaim 9, wherein the processor further causes the system to receive theincoming communication at the customer engagement center.
 11. The systemof claim 8, wherein the client sentiment is generated as a set ofcommunication metadata.
 12. The system of claim 11, wherein theselection of at least one customer service representative designation isalso based on the set of communication metadata including the clientsentiment.
 13. The system of claim 8, wherein the at least one customerservice representative designation is at least one of a CSR, a CSRgroup, a queue, or a queue group.
 14. The system of claim 8, wherein theprocessor further causes the system to provide at least one of theincoming communication or the client sentiment to the customer servicerepresentative designation.
 15. A non-transitory computer readablemedium programmed with computer readable code including instructionsthat when executed by a processor of a system, causes the system toexecute a method for routing based on client sentiment comprising:generate the client sentiment for an incoming communication based on ananalysis of the incoming communication; select at least one customerservice representative (CSR) designation to receive the incomingcommunication based on the client sentiment and a set of routinganalytics rules (RAR); perform an analysis of the set of RAR based onthe client sentiment and the selected at least one CSR designation; andupdate the set of RAR based on the analysis.
 16. The non-transitorycomputer readable medium of claim 15, wherein the method to be executedfurther comprises provide a customer engagement center system, thecustomer engagement center system is configured to receive incomingcommunications from outside of the customer engagement center system.17. The non-transitory computer readable medium of claim 16, wherein themethod to be executed further comprises receive the incomingcommunication at the customer engagement center.
 18. The non-transitorycomputer readable medium of claim 15, wherein the client sentiment isgenerated as a set of communication metadata.
 19. The non-transitorycomputer readable medium of claim 18, wherein the selection of at leastone customer service representative designation is also based on the setof communication metadata including the client sentiment.
 20. Thenon-transitory computer readable medium of claim 15, wherein the atleast one customer service representative designation is at least one ofa CSR, a CSR group, a queue, or a queue group.